The goal of machine condition monitoring is to detect machine failures at an early stage so that maintenance can be carried out in a timely manner. There are several strategies in use to perform machine maintenance. In a first strategy called corrective maintenance, maintenance is performed only if the machine fails. In another strategy called preventive maintenance, maintenance is performed on a pre-scheduled basis. Those first two approaches are easy to implement, but do not provide the best efficiency.
The present disclosure focuses on a third strategy, called predictive maintenance or condition-based maintenance. According to that strategy, maintenance is performed only if necessary. Predictive maintenance offers the highest economic efficiency, but also presents the largest challenge: how to ascertain whether a machine is working normally or abnormally (i.e., in a fault condition or a condition requiring maintenance). Another challenge is that, in the case of failure, the cause of that failure must be determined so that corresponding localized maintenance can be applied.
There are fundamentally two ways to address the fault diagnosis task necessary to perform predictive maintenance. First, a rule-based technique is perhaps the most widely used condition monitoring approach. Rule-based machine monitoring systems are described, for example, in G. Schreiber, H. Akkermans, A. Anjewierden, R. de Hoog, N. Shadbolt, W. V. De Velde and B. Wielinga, “Knowledge Engineering and Management: The Common KADS Methodology” 187-214 (MIT Press 2000); and M. Todd, Stephen D. J. McArthur, James R. McDonald and S. J. Shaw, “A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge,” IEEE Trans. on Systems, Man, and Cybernetics, part C, vol. 37, no. 5 at 979-992 (2007).
In rule-based predictive maintenance, a set of rules is used to analyze features or conditions of a machine. The general format of those rules is “If a condition, then a fault type.” Rules are typically derived by human experts who possess the knowledge of the underlying system model. Rules offer the user a transparency as to why a particular conclusion is reached via the exploration of the rule condition.
The design of accurate rules, however, is a very deliberate and time consuming process, especially for complex systems with many sensors and fault types. In one example, it required 80 man-years to develop one of the most commercially successful condition monitoring rule bases (L. Trave-Massuyes and R. Milne, “Gas-turbine condition monitoring using qualitative model-based diagnosis,” IEEE Expert, vol. 12, no. 3 at 22-31 (May/June 1997)).
A second approach to condition monitoring is the use of machine learning. Supervised-pattern-recognition-based techniques such as neural networks have received much attention recently in that area (M. J. Embrechts, and S. Benedek, “Hybrid identification of nuclear power plant transients with artificial neural networks,” IEEE Trans. on Industrial Electronics, vol. 51, no. 3 at 686-693 (June 2004)). Machine learning models are data-driven: they are learned from training data representing the normal condition and each of the fault types. This contrasts to the knowledge-based rule approach.
Machine learning algorithms can be learned very fast (for example, in minutes), are very accurate and are easily transferable between different machines. An obstacle to the extensive use of machine learning, however, is that it is usually difficult to obtain a sufficiently large training dataset. Obtaining training data representing the normal condition is usually straightforward because a machine typically operates normally during most of its lifespan. Obtained training data representing each of the fault types, however, is challenging because certain types of faults may occur only rarely, even if data from multiple similar machines is considered.
Several efforts have been made to combine supervised machine learning with a rule-based model. For example, in Z. Wang, Y. Liu and P. J. Griffin, “A combined ANN and expert system tool for transformer fault diagnosis,” IEEE Power Engineering Society Winter Meeting 1261-1269 (2000), the results from a neural network are combined with a rule base. There is no attempt to improve either the neural network or the rule base. In M. Todd, Stephen D. J. McArthur, James R. McDonald and S. J. Shaw, “A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge,” IEEE Trans. on Systems, Man, and Cybernetics, Part C, vol. 37, no. 5 at 979-992 (2007), machine learning is used to assist the design of rules.
There is presently a need to overcome the above described limitations of existing machine monitoring solutions where only limited fault data is available. There is furthermore a need for a machine monitoring solution that incorporates advantages from both rule-based systems and machine learning systems, while overcoming the most serious limitations of those systems.